The present invention relates to analysis of medical images and, more particularly, but not exclusively to automatic analysis and classification of medical images depicting an organ or a human body system.
Systems and devices for visualizing the inside of living organisms are among the most important medical developments in the last thirty years. Systems like X-ray scanners, computerized tomography (CT) scanners and magnetic resonance imaging (MRI) scanners allow physicians to examine internal organs or areas of the body that require a thorough examination. In use, the visualizing scanner outputs a medical image, such as a cross-sectional image, or a sequence of computerized cross-sectional images of a certain body organ, which is then diagnosed by radiologists and/or other physicians.
In most hospitals and radiology centers, the medical images are transferred to a picture archiving communication system (PACS) before being accessed by the radiologists. The PACS is installed on one or more of computers, which are dedicated for storing, retrieving, distributing and presenting the stored 3D medical images. The 3D medical images are stored in an independent format. The most common format for image storage is digital imaging and communications in medicine (DICOM).
The rapid growth of computerized medical imagery using PACS in hospitals throughout the world led to the development of systems for classifying visual medical data. For example, International Patent Application Publication No. WO/2007/099525, filed in Feb. 18, 2007 describes a system for analyzing a source medical image of a body organ. The system comprises an input unit for obtaining the source medical image having three dimensions or more, a feature extraction unit that is designed for obtaining a number of features of the body organ from the source medical image, and a classification unit that is designed for estimating a priority level according to the features.
Another example is described in U.S. Pat. No. 6,754,675 filed on Jul. 16, 2001 which describes image retrieval system contains a database with a large number of images. The system retrieves images from the database that are similar to a query image entered by the user. The images in the database are grouped in clusters according to a similarity criterion so that mutually similar images reside in the same cluster. Each cluster has a cluster center which is representative for the images in it. A first step of the search to similar images selects the clusters that may contain images similar with the query image, by comparing the query image with the cluster centers of all clusters. A second step of the search compares the images in the selected clusters with the query image in order to determine their similarity with the query image.